Exposure Levels and Determinants of Softwood Dust Exposures in BC Lumber Mills, 1981–1997
Bibliographic record
Abstract
Measurements of personal exposure to wood dust (n = 1237) collected by the Workers' Compensation Board of British Columbia, Canada, over the period 1981-1997 were used to construct an empirical model to identify broad determinants of softwood dust exposure. Potential determinants of exposure examined included species of tree processed; company; geographic location of lumber mill; department; job title; calendar year; and production factors such as board feet of lumber produced per year. A determinants of exposure model was built using multiple linear regression. Nested within this compliance database was a subset of samples collected for a research study. These enabled the authors to explore whether differences in exposure measurements can in part be explained by sampling strategy (research versus compliance). Potential differences were examined by examining differences in means for each job title, stratified by sampling strategy; and by offering "sampling strategy" as a categorical predictor variable to the empirical model. Multiple linear regressions revealed the most important determinants of increased wood dust exposure to be mill location away from the coast, earlier calendar year, and indoor jobs. The empirical model had an R2 of 0.39 and a predictive range from 0.02 to 25.45 mg/m3. Research and compliance sampling strategies showed no difference in mean exposure and distribution in the empirical model (p < 0.05), suggesting that regulatory exposure databases may be of utility for exposure assessment in epidemiology. This research indicates that compliance-sampling strategies do not result in an overestimation of mean exposure levels within jobs, but they do focus on a biased sample of jobs-those most highly exposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".